The Hopfield model and its role in the development of synthetic biology

Neural network models make extensive use of concepts coming from physics and engineering. How do scientists justify the use of these concepts in the representation of biological systems? How is evidence for or against the use of these concepts produced in the application and manipulation of the models? It will be shown in this article that neural network models are evaluated differently depending on the scientific context and its modeling practice. In the case of the Hopfield model, the different modeling practices related to theoretical physics and neurobiology played a central role for how the model was received and used in the different scientific communities. In theoretical physics, where the Hopfield model has its roots, mathematical modeling is much more common and established than in neurobiology which is strongly experiment driven. These differences in modeling practice contributed to the development of the new field of synthetic biology which introduced a third type of model which combines mathematical modeling and experimenting on biological systems and by doing so mediates between the different modeling practices

How can history of science matter to scientists?

History of science has developed into a methodologically diverse discipline, adding greatly to our understanding of the interplay between science, society, and culture. Along the way, one original impetus for the then newly emerging discipline —- what George Sarton called the perspective “from the point of view of the scientist” -— dropped out of fashion. This essay shows, by means of several examples, that reclaiming this interaction between science and history of science yields interesting perspectives and new insights for both science and history of science. The authors consequently suggest that historians of science also adopt this perspective as part of their methodological repertoire

Synthetic Modeling and the Functional Role of Noise

In synthetic biology the use of engineering metaphors to describe biological organisms and their behavior has become a common practice. The concept of noise provides one of the most compelling examples of such transfer. But this notion is also confusing: While in engineering noise is a destructive force perturbing artificial systems, in synthetic biology it has acquired an additional functional meaning. It has been found out that noise is an important factor in driving biological processes such as gene regulation, development, and evolution. How did noise acquire this dual meaning in the field of synthetic biology? In this paper we study the emergence of the functional meaning of noise in relation to synthetic modeling. We will pay particular attention to the interdisciplinary aspects of this process highlighting the way borrowed concepts, analogical reasoning and the use of cross-disciplinary computational templates were entwined in it

Synthetic Modeling and Mechanistic Account: Material Recombination and Beyond

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. While we appreciate Bechtel and Abrahamsen’s point that mathematical/computational models are used to provide dynamic mechanistic explanations, we think that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, as we will show, synthetic modeling as a kind of material recomposition strategy also points beyond the mechanistic paradigm

Mathematization in Synthetic Biology: Analogies, Templates, and Fictions

In his famous article “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” Eugen Wigner argues for a unique tie between mathematics and physics, invoking even religious language: “The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve”. The possible existence of such a unique match between mathematics and physics has been extensively discussed by philosophers and historians of mathematics. Whatever the merits of this claim are, a further question can be posed with regard to mathematization in science more generally: What happens when we leave the area of theories and laws of physics and move over to the realm of mathematical modeling in interdisciplinary contexts? Namely, in modeling the phenomena specific to biology or economics, for instance, scientists often use methods that have their origin in physics. How is this kind of mathematical modeling justified

Synthetic Modeling and Mechanistic Account: Material Recombination and Beyond

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. While we appreciate Bechtel and Abrahamsen’s point that mathematical/computational models are used to provide dynamic mechanistic explanations, we think that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, as we will show, synthetic modeling as a kind of material recomposition strategy also points beyond the mechanistic paradigm

Magnets, spins, and neurons: The dissemination of model templates across disciplines

One of the most conspicuous features of contemporary modeling practices is the dissemination of mathematical and computational methods across disciplinary boundaries. We study this process through two applications of the Ising model: the Sherrington-Kirkpatrick model of spin glasses and the Hopfield model of associative memory. The Hopfield model successfully transferred some basic ideas and mathematical methods originally developed within the study of magnetic systems to the field of neuroscience. As an analytical resource we use Paul Humphreys's discussion of computational and theoretical templates. We argue that model templates are crucial for the intra- and interdisciplinary theoretical transfer. A model template is an abstract conceptual idea associated with particular mathematical forms and computational methods